Network camera systems, for example video surveillance network camera systems and IP camera systems, have existed for a number of years but have undergone relatively slow industry adoption. Compared to traditional analog camera systems, network camera systems offer advantages such as accessibility, integration, low installation costs, scalability, and an ability to move to higher resolution video. Video visual content and data produced by network cameras, however, demand large amounts of bandwidth and storage capacity.
Bandwidth problems associated with network camera systems have led to more complex camera networks that include an increased number of switches and, in some cases, complete alternative data paths. Storage problems associated with network camera systems become magnified as video resolution and the number of cameras in a system increase. For example, a single standard 01 resolution camera using MPEG-4 compression and operating at 30 frames-per-second (fps) can require 360 gigabytes (GB) of storage for video data representing one month of video data. A camera system with 1000 cameras, therefore, would require 360 terabytes (TB) of storage for data spanning one month. This example demonstrates a huge cost and facility management challenge presented with network camera systems, especially where mega-pixel resolution is desired and where applications require six months or a year of video data storage. Due to the problems identified, most network video data are not recorded at full quality, but are recorded at lower resolutions and frame rates. Because typical high resolution cameras generate video data requiring a large amount of storage resources within a short period of time, it is impractical for a typical camera to include a self-contained storage unit, such as a hard drive, that is able to store a significant amount of video data.
Typical storage architecture of network camera systems is configured with central storage similarly to traditional analog systems. The architecture includes centrally located digital video recorders (DVRs) or network video recorders (NVRs) connected through a network to IP cameras. The typical architecture for IP cameras is inadequate for a number of reasons. If, for example, the network fails or is made nonoperational for maintenance or any other reason, all video is lost and can never be retrieved. Numerous (e.g., many dozens of) cameras streaming across the network to a central storage device place severe bandwidth demands on the network. Moreover, 99% of the bandwidth used is wasted because typically less than 1% of the video is ever accessed for review. Additionally, typical network camera systems often lack storage scalability such that, as network camera systems expand, central storage systems require “forklift” upgrades.
Another problem with typical video data storage configurations is that many applications require storage devices to continuously run. Such continuous operation causes the storage devices to fail after three to five years of operation. Unless archived or stored redundantly, data on failed storage devices become lost. The need to replace storage devices, therefore, becomes a significant concern and maintenance issue.
Recently, some network camera systems have implemented video analytics processing to identify when important events (such as object movement) are being captured by a video camera. Video analytics has been primarily used to alert security of potential unwanted events. Most video analytics is performed by a central processor that is common to multiple cameras, but some video cameras have built-in video analytics capabilities. These video cameras with built-in analytics, however, have not included large capacity storage due to the large storage requirements of the video data generated by the camera and the traditional approach of centralized storage. Also, there are some cameras configured without built-in video analytics but with built-in small storage capacity that is insufficient to serve as a substitute for traditional DVRs and NVRs. Moreover, if the video data are stored only in the camera, the stored video data are vulnerable to attack or being stolen.
Computer networking devices, such as switches and routers, are at the heart of any network infrastructure and are central to a network's quality of service (QoS). QoS in a network is assessed by a combination of the following metrics: (1) speed—information should be delivered at the fastest possible speed from the source to its destination (sink); (2) scalability—as more nodes (sources, sinks, switches and routers) are added, the network should scale seamlessly and automatically adapt to increased loads, increased resources and increased network routing options; (3) redundancy and fault-tolerance—if nodes in the network fail, systems should still be able to function normally. Additionally, redundancy in the network should improve data access times during periods of heavy loads.
Packet switching and routing have traditionally been non-discriminatory, content-agnostic processes in networks. But as high-speed data links facilitate sophisticated and network-intensive applications, switches and routers are becoming increasingly content aware. For example, some recent routers analyze data packet contents for the existence of computer viruses and worms, and monitor application specific traffic patterns for denial of service attacks. Thus, rudimentary content-awareness has been known to improve network security.
Content-awareness can also promote quality of service in a network. For example, some specialized routers are optimized for video streaming. Such routers are designed to handle multicasting and unicasting from a small set of servers to a large number of clients. These routers are also capable of transcoding video streams depending on the content and the capabilities of the client. The specialized routers treat video and audio differently from other packets of information by categorizing data packets as either non-time-critical data or real-time video and audio.
Therefore, a need exists for a network camera system that produces high quality video data, requires less storage capacity and network bandwidth, meets IT standards, is easily scalable, and operates for a longer period of time without storage device replacement.